Evaluating the effects of brain injury, disease and tasks on cognitive fatigue

Because cognitive fatigue (CF) is common and debilitating following brain injury or disease we investigated the relationships among CF, behavioral performance, and cerebral activation within and across populations by combining the data from two cross-sectional studies. Individuals with multiple sclerosis (MS) were included to model CF resulting from neurological disease; individuals who had sustained a traumatic brain injury (TBI) were included to model CF resulting from neurological insult; both groups were compared with a control group (Controls). CF was induced while neuroimaging data was acquired using two different tasks. CF significantly differed between the groups, with the clinical groups reporting more CF than Controls—a difference that was statistically significant for the TBI group and trended towards significance for the MS group. The accrual of CF did not differ across the three groups; and CF ratings were consistent across tasks. Increasing CF was associated with longer response time for all groups. The brain activation in the caudate nucleus and the thalamus was consistently correlated with CF in all three groups, while more dorsally in the caudate, activation differed across the groups. These results suggest the caudate and thalamus to be central to CF while more dorsal aspects of the caudate may be sensitive to damage associated with particular types of insult.

Fatigue is an everyday occurrence, yet remains poorly understood.Furthermore, while transient fatigue is ubiquitous in the general population, fatigue is pervasive, persistent, and debilitating for clinical groups such as individuals with multiple sclerosis (MS) and traumatic brain injury (TBI).Indeed, individuals who have sustained a TBI rate fatigue as their worst symptom 1 , contributing to a lower quality of life 2 .Similarly, individuals with MS rate fatigue as amongst their most debilitating symptoms 3 , interfering with activities of daily living and resulting in a lower quality of life 4 .Moreover, in both MS and TBI populations, fatigue has been linked with cognitive impairment [5][6][7][8] .
While the importance of understanding fatigue in neurological populations has long been appreciated 2,3 , fundamental knowledge gaps remain.For example, it is unclear if fatigue experienced by individuals with neurological disorders is different from fatigue experienced by healthy individuals (Controls), and if different populations experience different 'baseline' levels of fatigue and/or different accrual rates of fatigue.Moreover, the relationships among fatigue, behavioral performance, and activation in associated brain regions within and across populations are poorly characterized.Addressing these issues is critical to develop a clear understanding of what fatigue is and to understand its role in cognition, both of which are necessary to develop effective treatments.
Beyond the knowledge gaps pointed out above, fatigue-related research has been hampered by the fact that "fatigue" is an umbrella term, covering several domains.Here we focus on cognitive fatigue (CF), or fatigue resulting from cognitive work.Additionally, we primarily focus on state CF, or CF that accrues over a relatively short timeframe (e.g., the past four minutes).Trait CF measures, by contrast, focus on CF reported to have accrued over a longer timeframe (e.g., the past four weeks).While trait CF measures may provide a good index of baseline CF levels, they may also be contaminated by other factors (e.g., depression, medication and deconditioning).Indeed, the correlation between trait CF measures and depression is well docuemented [39][40][41][42] .In contrast, similar associations between state CF and depression have not been found 9 .Recent research using state CF measures Table 1.The demographics of the sample.Age and Education are reported in years; MFIS cognitive refers to the cognitive subscale of the Modified Fatigue Impact Scale; Depression refers to the score on the Chicago Multiscale Depression Inventory total score; Disease Duration (MS) and Time Since Injury (TBI) is reported in months.*Symbol represents a statistically significant difference from the Control group.† Symbol represents a statistically significant difference between the MS and TBI groups.disorder), MRI contraindications, or psychoactive medication use.Participants were excluded if they had any significant neurological history (e.g., head injury, stroke, seizures) with the exception of MS and TBI for the respective groups.While the two original studies included criteria for matching each clinical group to the control group, it was not practical to match all three groups across all demographic variables (e.g., MS is more prevalent in women while TBI is more prevalent amongst men 25,26 ).Additionally, because depression is more common in MS 27,28 and TBI 27,28 relative to Controls and because fatigue has been shown to covary with depression 29,30 , subjects completed the Chicago Multiscale Depression Inventory 31,32 .All procedures were approved by the Institutional Review Boards of Rutgers University and Kessler Foundation and all methods were performed in accordance with the relevant guidelines and regulations.All participants provided written informed consent, and all were remunerated $100 for each testing session.Individuals in the MS group were recruited from local MS clinics and the North Jersey Chapter of the National MS Society; all had a diagnosis of clinically definite MS 41 (mean disease duration = 135 months, with a majority having a relapsing-remitting disease course) and were exacerbation-free for at least 4 weeks prior to testing.For the TBI group severity was defined as the lowest Glasgow Coma Scale (GCS) rating in the first 24 h following injury 34 .When a GCS score was not available, subjects were included only when there was sufficient medical documentation that allowed for a post-hoc estimation of initial GCS, or if other confirmatory data (e.g., positive anatomic neuroimaging findings, focal neurologic signs) were available.The mean time since injury for the TBI group was 90.8 months, and the majority had experienced a severe TBI.The majority of TBIs were due to motor vehicle accidents (71%), followed by falls (23%) and assault (6%).

Behavioral data and paradigms
E-Prime software 35 was used to present stimuli and record responses for both tasks.The order of the two tasks was counterbalanced across participants, and approximately one week elapsed between testing sessions.The outcome variables from the behavioral tasks were RT and accuracy.For the analyses of the RT and fMRI data, only correct trials were included.

Working memory task
The 2-back condition of the n-back working memory paradigm was used to induce CF 36 , based on the literature showing working memory deficits following TBI 37,38 .Participants completed four blocks of the task, during which behavioral and fMRI data were acquired.To ensure all participants had comparable levels of proficiency, the task was practiced to criterion (80% correct) prior to scanning.Each block was comprised of 65 trials during which a single letter was presented at the center of the screen and participants were asked to press a button with their right index finger every time the letter was the same as the letter presented two trials previously in the sequence (e.g., R N Q N…).All participants were asked to respond as quickly as possible without sacrificing accuracy.Letters (A B C D F H J K M N P Q R S T V Z, presented with equal frequency) were presented in white on a black background (Arial 72-point font), and remained on the screen for 1.5 s, followed by an inter-trial interval (ITI) of 500 ms during which a white fixation cross was presented.The time between successive trials was jittered to optimize later deconvolution of the fMRI data using the Optseq2 program (https:// surfer.nmr.mgh.harva rd.edu/ optseq/).The jittering was achieved by adding between zero and six 2-s-long intervals to each ITI.The intervals were drawn from a power distribution such that most were zero (i.e., ITI = 500 ms), followed by one (i.e., ITI = 2 s) and so on.The average ITI was 1587.9 ms (± 1769.7 SD), and each block lasted 4 min.30 s.

Processing speed task
The modified Symbol-Digit Modalities Test (mSDMT) was used as the other CF-induction task because of the centrality of processing speed deficits in MS 16,39 .All participants practiced the task to criterion (80% correct) to ensure proficiency before working through four blocks inside the scanner while fMRI data were acquired.
On each trial of the mSDMT, participants were presented with a reference grid at the top of the screen that had two rows and nine columns 23 .In the top row the numbers 1-9 were presented, and nine unique symbols were presented in the row beneath.In the bottom part of the screen a probe stimulus was presented that consisted of one number and one symbol.Subjects reported whether the probe number-symbol pair matched a numbersymbol pair in the reference grid.Each trial lasted 4 s, and 55 trials were presented in each block.The Optseq2 was used to optimize the trial sequence by inserting between zero and six 2-s-long intervals between the mSDMT trials.The average ITI was 6139.7 ms (± 3544.2SD), and each block lasted 7 min.50 s.

State CF
State CF was assessed at baseline and after each block of the two tasks using a visual analogue scale of fatigue (VAS-F) 40,41 .Participants were asked: "How mentally fatigued are you right now?" and rated their CF on a scale from 0 to 100 (with 0 being not at all fatigued and 100 being maximally fatigued) by reporting a numerical value to the experimenter.TA was used as an index of cumulative damage to the central nervous system (CNS) based on both the MS 61 and TBI 43,44 literatures.Because of the central role the thalamus plays in brain function, TA represents a biomarker of neurodegeneration and associated dysfunction/decline 45 , and has been shown to be more sensitive to neurodegeneration early in the course of diseases such as MS than other, more global measures 46 .Because we had an hypothesis about the caudate nucleus, we also investigated atrophy of the caudate nucleus.As a manipulation check, we also calculated another measure of brain atrophy -brain parenchymal fraction (BPF) -for each participant to ensure that TA and BPF produced congruent results.

Neuroimaging acquisition
FreeSurfer 47 was used to segment each participant's MPRAGE, resulting in an estimate of right and left thalamic and caudate volumes as well as BPF.Thalamic and caudate volumes were converted into z-scores using publicly available age-, sex-, and intracranial volume-adjusted normative values for thalamic volume 48 .The z-scores were then averaged to estimate each participant's TA and caudate atrophy (CA), where positive z-scores represented larger than expected volume (in relation to the normative sample) and negative values represented smaller than expected volumes.

Analyses
Statistical analyses were conducted using the R statistical package (version 3.4.3).Prior to analysis, the normality of all variables was assessed by visual inspection and the Agostino test 49 .In those cases when the data were found to be skewed, they were transformed using the Box Cox method 50 .

Demographics
Differences between the groups in age, education and depression were tested with one-way ANOVAs.The factor was Group (MS, TBI, control).Differences in sex across the groups was assessed using a Chi-square test.All statistically significant differences were included in the group level analyses as covariates.

State CF
For the analysis of the VAS-F scores, a Linear Mixed Effects analysis (LME) was used.Group (MS, TBI, control) and Sex (men vs. women) were between-subjects factors; Task (2-back vs. mSDMT) and Rating (VAS-F rating acquired at baseline and after each task: 5 ratings per task) were fixed effects; age was a quantitative variable; and Participant was included as a random factor.
For analyses in which we were interested in the relationship between CF and another dependent measure (RT, accuracy and functional activation), the VAS-F scores obtained before and after each task block were averaged to provide an estimate of the amount of CF during each block.Because the VAS-F scores were skewed, they were transformed before being mean-centered for each group.

Rate of CF increase and stability of CF across task
To investigate the stability of the rate of increase in CF across the four runs of the two tasks, a regression line was fit to the five VAS-F scores for each task and participant 51 .The slope of this regression line was operationalized as the CF rate (CFR) and the correlation of the CFR across tasks was calculated with a Pearson's correlation.Additionally, the extent to which the CFR differed across participants was investigated using an LME with the factors of Group, and Task (as above), with Sex and Age included as covariates.
Similarly, the stability of participants' CF level prior to undertaking the task was assessed by correlating the intercept of the regression line calculated for each task and participant.And, as with the slope data, an LME model was used to assess whether the intercept varied across groups and tasks.

The relationship between CF and behavioral performance
Response time and accuracy were analyzed with an LME that included the factors of Group (MS, TBI, control), Task (2-back, mSDMT), Run (runs 1-4), and Sex (women vs. men); the VAS-F scores and age were included as quantitative variables; participant was included as a random factor.For RT, only RTs from correct trials were included in the analysis.Accuracy was calculated as the number of trials on which the correct response in each block was made divided by the total number of trials in that block.

The relationship between state CF and trait CF
To investigate whether state and trait measures of CF were related to one another, two linear regressions were used.VAS-F scores were used as the measure of state CF and the cognitive subscale of the Modified Fatigue Impact Scale (MFIS cognitive ) was used as the measure for trait CF.One regression was run for each task (2-back and mSDMT), and in each the average VAS-F score (across the five ratings) was predicted by MFIS cognitive score.Group, age and sex were also included in each model.As a manipulation check, a one-way ANOVA was also used to test for differences in MFIS cognitive across the groups; age and sex were included as covariates.

The relationship between CF and brain atrophy
One-way ANOVAs were used to test whether TA and CA differed across groups.ANCOVAs were then used to test whether there was a relationship between state CF and TA and between state CF and CA.The factors for this ANCOVA were Group (MS vs. TBI), and Sex (men vs. women); and the VAS-F scores (averaged across the runs) and age were included as quantitative variables.The same ANCOVA was used for the BPF data.The relationship between CF and brain activation fMRI: The neuroimaging data were preprocessed using fMRIPrep 1.4.1 RRID:SCR_016216,52 , which is based on Nipype 1.2.0 RRID:SCR_002502,53 .This included segmentation and warping of the T1-weighted anatomical scan into standard space (using the MNI template).The functional MRI data was motion-corrected, smoothed, scaled and warped into the same standard anatomical space.The details of these preprocessing steps can be found in the supplemental data section.An LME was used for a whole-brain analysis (3dLMEr from the AFNI suite of processing tools) with Task (2-back vs. mSDMT), Sex (men vs. women) and Run (runs 1-4) as factors; the VAS-F scores, and age were included as quantitative variables; participant was included as a random factor.As in previous work 15,21,54,55 only runs resulting in CF were included in the analysis (i.e., runs in which the VAS-F score was greater than zero).This resulted in the exclusion of ~ 20% of runs (see supplemental data for details).
The results of this analysis were corrected for multiple comparisons by using an individual voxel probability threshold of p < 0.005 and a cluster threshold of 28 voxels (voxel dimension = 2.4 × 2.4 × 4 mm).Monte Carlo simulations, using 3dClustSim (version AFNI_21.3.04,compile date: Oct 20, 2021) showed this combination resulted in a corrected whole brain alpha level of p < 0.05.Using the same approach, we calculated the corrected alpha level for the striatum and thalamus (about which we had prior hypotheses) to be nine voxels.

In-scanner movement
Differences in in-scanner movement were assessed by identifying the maximum framewise displacement (FD) from the realignment step for each run of each task for each participant, exclusive of those frames that were censored from the deconvolution (first level analysis).The remaining FD data were analyzed with an LME that included the factors of Group (MS, TBI, control), Task (2-back, mSDMT), Run (runs 1-4), and Sex (women vs. men); age was a quantitative variable and participant was included as a random factor.

State CF
Visual inspection of Fig. 1A suggests that there were differences in VAS-F scores between the groups, that the VAS-F scores increased across successive ratings, and that these two factors (Group and Rating) did not interact.The results supported this interpretation.The main effect of Group was significant (F(2,77) = 3.63, p = 0.03, www.nature.com/scientificreports/η 2 partial = 0.09): controls reported the least CF, followed by individuals with MS, and individuals who had sustained a TBI reported the most (see Fig. 1A).Of the pairwise differences, the Control group (95% CI [7.6, 28.9]) differed significantly from the TBI group (95% CI [28.0, 49.3]) (t(77.9)= 2.49, p = 0.01) and trended toward significance from the MS group (95% CI [21.7, 43.2]) (t(76.7)= 1.89, p = 0.06); the MS and TBI groups did not significantly differ from one another.The main effect of Task was significant (F(1,630.4)= 4.00, p = 0.05, η 2 partial = 0.006), which was due to higher VAS-F ratings for the mSDMT task (31.5) than for the 2-back task (28.1).The main effect of Rating was significant (F(4,222.9)= 2.50, p = 0.045, η 2 partial = 0.04) which was due to all three groups reporting increasing CF over time (i.e., across the five VAS-F ratings: 19.0, 24.4,29.3, 36.0,40.3; see Fig. 1A).No other effects or interactions reached conventional levels of significance.

Rate of CF increase and stability of CF across task
There was a significant positive correlation between CFR across the two tasks (r = 0.50, p < 0.001) (see Fig. 1B): high CFR on the 2-back task was associated with a high CFR on the mSDMT.There was also a significant positive correlation between the intercepts across the two tasks (r = 0.70, p < 0.001) (see Figure S1): a high intercept for the 2-back task was associated with a high intercept for the mSDMT.The LME testing whether the three groups differed on CFR showed no significant effects.In contrast, the result of the LME of the intercept data showed a significant effect of Group (F(2,74) = 3.18, p = 0.047, η 2 partial = 0.08).The intercept, or baseline level of CF, of the control group was lowest (mean = 14.3, 95% CI [4.3, 24.4]), the intercept of the MS group was higher (mean = 26.4,95% CI [16.2, 36.5]) and the intercept of the TBI group was highest (mean = 32.8,95% CI [22.8,  42.8]).This result was consistent with the VAS-F data shown in Fig. 1A.Of the pairwise comparisons, only the difference between the TBI and control groups was significant (t(74) = 2.43, p = 0.046).
All three groups performed the tasks with greater than 90% accuracy.There was nevertheless a main effect of Group (F(2, 64.7) = 4.69, p = 0.01, η 2 partial = 0.13): controls responded with the most accuracy (95.6% [0.956], 95% CI [0.94, 0.98]), individuals with MS (91.9% [0.919], 95% CI [0.90, 0.94]) and TBI (92.5% [0.925], 95% CI [0.91, 0.94]) responded with somewhat lower accuracy.Only the difference between the MS and Control group was significant (p < 0.05).The main effect of Task was significant (F(1, 381.8) > 100, p < 0.001, η 2 partial = 0.45) and was due to participants responding with more accuracy on the mSDMT (97.0%[0.97], 95% CI [0.96, 0.98]) than the 2-back task (89.7% [0.897], 95% CI [0.89, 0.91)).There was an interaction between Group and Task (F(2, 382.3) = 13.74,p < 0.001, η 2 partial = 0.07).This can be seen in Figure S2 and was due to all three groups being comparably accurate on the mSDMT task (p > 0.05 for all pairwise comparisons), but for the 2-back task the control group was more accurate than the MS or the TBI groups (p < 0.01 in both cases; the accuracy of MS and TBI groups did not significantly differ).There was also an interaction between Task and VAS-F (F(1, 390.6) = 6.21, p = 0.01, η 2 partial = 0.02).For both tasks there was a negative relationship between VAS-F and accuracy and the relationship was larger for the 2-back task (coefficient = − 0.03) than for the mSDMT (− 0.0000009).Finally, the three-way interaction between Group, Task and VAS-F was significant (F(2, 387.9) = 3.70, p = 0.025, η 2 partial = 0.02).As shown in Figure S3, this was due to the relationship between accuracy and CF being negative for both tasks for the Control group, while for the MS group the relationship was negative for the 2-back task but positive for the mSDMT and for the TBI group, the relationship was slightly positive for both tasks (the coefficients for each Group-Task pairing are shown in Figure S3).Of the pairwise comparisons, the only significant difference was between the slopes for the two tasks in the MS group (p < 0.01).

The relationship between state CF and trait CF
The relationship between VAS-F and MFIS cognitive was not significant for either task.However, as Table 1 suggests, there were significant differences in MFIS cognitive across the groups (F(2,65) = 20.50,p < 0.001, η 2 partial = 0.39), with both the MS and TBI group reporting significantly more trait CF than the Control group (in both cases, t(65) ≥ 5.37, p < 0.001).The MS and TBI groups did not significantly differ in their MFIS cognitive scores (t(65) = 0.17).
While the MS and TBI groups had similar levels of TA and CA, the relationship between these measures of brain atrophy and state CF may nevertheless have differed between the two groups.To test this, we analyzed the relationship between state CF and TA, accounting for Group (MS vs. TBI), age and sex.This analysis showed a significant effect of TA on VAS-F (F(1,56) = 5.84, p = 0.02, η 2 partial = 0.09) which was due to a positive relationship (coefficient = 5.93): the more TA there was, the less CF was reported (see Fig. 1C).No other effects or interactions were significant, suggesting that the relationship between CF and TA was similar for the MS and TBI groups.The same analysis was run on the CA data and yielded no significant results.

The relationship between CF and brain activation
In-scanner movement The main effect of Group was not significant in the analysis of the FD data, nor did it interact with any of the other variables.fMRI There was a main effect of state CF (VAS-F) in the thalamus and the caudate nucleus (see Table 2 and Fig. 2A,C).As Fig. 2B shows, the relationship was complimentary, with activation in the thalamus increasing as CF increased and activation in the caudate decreasing as CF increased (see also Table 2).
The effect of VAS-F was modified by Group in the caudate nucleus of the basal ganglia (see Table 3 and Fig. 2D,E).As Fig. 2E shows, there was a negative relationship between activation in the caudate and VAS-F for the Control group, a weakly negative relationship for the MS group and a positive relationship for the TBI group.This pattern was also evident in another area more dorsal in the caudate nucleus (see Table 3).
For completeness, task related activation and differences are shown in Figure S4 and Table S2.

Discussion
This is the first study to compare CF and its neural underpinnings across clinical groups and across tasks.We expected the MS and TBI groups to report comparable levels of CF that were significantly greater than the CF reported by the control group.The results largely supported these hypotheses, with the MS and TBI groups reporting higher levels of fatigue than the control group (the difference between the TBI and control group was significant at p < 0.05 while the difference between the MS and control group only trended towards significance at p = 0.06).These differences provide support to the idea that CF experienced by clinical groups is similar and that this 'pathological' CF differs from the CF experienced by the control group.While the clinical groups reported more CF than controls, the rate of increase of CF was comparable across the three groups as we had hypothesized.That is, brain injury or disease resulted in a shift of the baseline level of CF (as shown by the differences in the intercept of the regression line fitted to the VAS-F scores; see also Fig. 1A), but did not affect the rate at which state CF accrues during task performance.This suggests that higher CF reported by clinical samples ('pathological fatigue') results from a baseline shift rather than from different accrual rates of CF during task performance.
In previous work, we have argued that CF is related to a change in the balance between effort and reward 54,57,58 That is, CF is related to maintaining an aspect of homeostasis whereby the effort expended merits the reward received.This view is supported by the behavioral data in the current study: for all three groups, higher VAS-F scores were associated with longer latencies as hypothesized, suggesting gradual exhaustion of cognitive resources with repeated task performance.It is also supported by the positive relationship between greater TA and lower VAS-F scores in the clinical groups, as well as by the fMRI data, wherein higher VAS-F scores were associated with less activation in the caudate nucleus and more in the thalamus.These results accord well with what is known of striato-thalamic connections.Not only does the striatum project to the thalamus 59 , but the connections are inhibitory such that more activation in the striatum would be expected to result in less activation in the thalamus 59 .
Our results show some of the important nuances of CF in persons who have sustained a brain injury or have a neurological disease.Individuals with TBI and MS did not differ in TA, suggesting a certain amount of similarity in the extent to which CNS damage resulted in widespread neuronal degradation.Moreover, individuals with TBI and MS did not differ in CFR, nor did each group's CFR differ from the control group, suggesting generality in the accrual of CF over time.Finally, individuals with TBI and MS did not differ in the amount of CF they reported, and individuals with TBI reported significantly more CF than Controls.The fMRI results accord well with the Table 2.The brain areas associated with the main effect of CF.X Y Z = the location of the voxel with peak intensity in each cluster; Vox refers to the number of voxels in the cluster; Χ 2 Stat refers to the maximal Χ 2 statistic in each cluster.Coefficient refers to the slope of the best fitting linear relationship between brain activation and VAS-F.similarities and differences in the behavioral results.For example, the CF-related activation in the thalamus and the ventral aspect of the caudate nucleus was common to all three groups and both tasks, which may reflect common aspects of CF such as CFR.Furthermore, there were differences across the groups in the activation more dorsally in the caudate nucleus, which may help explain the differences in the intensity of CF across the groups.For the Control group, more activation in this region was associated with less CF, which was similar to the relationship between activation and CF for all three groups more ventrally in the caudate.However, for the TBI group, who reported the most CF, this relationship was flipped such that more activation was associated with more CF.This result is compatible with idea that TBI results in disruption of the dopaminergic system, which contributes to CF 58 .The findings from the MS group are also compatible with this hypothesis inasmuch as the relationship between activation in the caudate nucleus and CF falls between that of the Control and TBI groups and they report a numerically intermediate level of CF (the difference between the Control and MS groups in CF trended toward significance).Furthermore, the finding that the group-level differences in activation in the caudate nucleus was found more dorsally in the caudate (relative to the more ventral caudate region that showed CF-related activation in all three groups) is consistent with literature showing a ventral-dorsal gradient

Figure 1 .
Figure 1.The effects of cognitively fatiguing tasks on ratings of fatigue (VAS-F scores).(A) The VAS-F scores for the three groups (HC shown in red filled circles, MS shown in green open circles and TBI shown in blue open circles) for each of the five ratings.The 0th rating was collected prior to the first run of the fatigue induction task (the 2-back or the mSDMT task); the 1st rating was collected after the first run of the fatigue induction task; the 2nd rating was collected after the second run, and so on.Error bars represent standard error of the mean.(B) The significant positive correlation between the Cognitive Fatigue Rate (CFR) on the 2-back task and the mSDMT.(C) The significant positive correlation between brain atrophy, operationalized as thalamic atrophy, and the average VAS-F rating for the MS (green open circles) and the TBI (blue open circles) groups.For panels B and C the shaded areas represent 95% confidence intervals. https://doi.org/10.1038/s41598-023-46918-y

Figure 2 .
Figure 2. The effects of cognitively fatiguing tasks on brain activation.(A) The location in the caudate of brain activation showing the main effect of CF (green arrow).(B) The significant negative relationship between the CF (VAS-F scores) and brain activation in the caudate nucleus (green line) and the significant positive relationship between CF and brain activation in the thalamus (dark blue line).(C) The location of thalamic brain activation showing the main effect of CF (dark blue arrow).(D) The location of brain activation showing at interaction between Group (HC, MS and TBI) and CF: the caudate nucleus of the basal ganglia (blue arrow).(E) Activation in the caudate nucleus as a function of CF for each of the three groups; Controls are shown in red, the MS group in green and the TBI group in blue.For panels B and E the shaded areas represent 95% confidence intervals.

Table 3 .
The brain areas associated with the interaction of Group and VAS-F.X Y Z = the location of the voxel with peak intensity in each cluster; Vox refers to the number of voxels in the cluster; Χ 2 Stat refers to the maximal Χ 2 statistic in each cluster.The slope of the best fitting linear relationship (the coefficient) between brain activation and VAS-F for each group are shown at the right.